The value of introspective measures in aptitude-treatment interaction research

Author:

Sachs Rebecca1,Akiyama Yuka2,Nakatsukasa Kimi3

Affiliation:

1. Virginia International University

2. University of Tokyo

3. Texas Tech University

Abstract

Abstract To explore the value of introspective measures in aptitude-treatment interaction (ATI) research, this study analyzed the cognitive profiles and concurrent think-alouds of six university learners of Japanese who were highly successful, moderately successful, or unsuccessful under two computer-mediated feedback conditions in a larger (N = 80) quantitative ATI investigation (Sachs, 2011). That study had made indirect inferences regarding relationships among individual differences (IDs), cognitive processes, and learning on the basis of correlational results. Using Leow’s (2015) depth-of-processing (DoP) framework as a lens, what we found in the qualitative verbalization data highlighted that learners in the same condition with similar strengths in the IDs that are statistically associated with performance at the group level may nonetheless engage in different cognitive processes and achieve different learning outcomes, and vice versa. The findings also pointed toward more complex ID-DoP and ID-ID interactions that future research could explore, such as the possibility that a weakness in memory might limit the benefits of metalinguistic knowledge and analytic processing in a condition where group-level correlations suggest analysis is relevant to success, or that analytic processing might enhance the value of memory in a condition where memory is relevant to success. In our conclusions, we argue for the value of mixed-methods research in this area.

Publisher

John Benjamins Publishing Company

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analysis of Self-Regulated Learning Skills in Senior High School Students: A Phenomenological Study;TEM Journal;2021-08-27

2. Pedagogical Approaches in Adaptive E-learning Systems;2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);2020-06

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